chore: import upstream snapshot with attribution

mla/tests/test_regression_accuracy.py

@@ -0,0 +1,61 @@
+try:
+    from sklearn.model_selection import train_test_split
+except ImportError:
+    from sklearn.cross_validation import train_test_split
+from sklearn.datasets import make_regression
+
+from mla.knn import KNNRegressor
+from mla.linear_models import LinearRegression
+from mla.metrics.metrics import mean_squared_error
+from mla.neuralnet import NeuralNet
+from mla.neuralnet.layers import Activation, Dense
+from mla.neuralnet.optimizers import Adam
+from mla.neuralnet.parameters import Parameters
+
+# Generate a random regression problem
+X, y = make_regression(
+    n_samples=1000,
+    n_features=10,
+    n_informative=10,
+    n_targets=1,
+    noise=0.05,
+    random_state=1111,
+    bias=0.5,
+)
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.25, random_state=1111
+)
+
+
+def test_linear():
+    model = LinearRegression(lr=0.01, max_iters=2000, penalty="l2", C=0.003)
+    model.fit(X_train, y_train)
+    predictions = model.predict(X_test)
+    assert mean_squared_error(y_test, predictions) < 0.25
+
+
+def test_mlp():
+    model = NeuralNet(
+        layers=[
+            Dense(16, Parameters(init="normal")),
+            Activation("linear"),
+            Dense(8, Parameters(init="normal")),
+            Activation("linear"),
+            Dense(1),
+        ],
+        loss="mse",
+        optimizer=Adam(),
+        metric="mse",
+        batch_size=64,
+        max_epochs=150,
+    )
+    model.fit(X_train, y_train)
+    predictions = model.predict(X_test)
+    assert mean_squared_error(y_test, predictions.flatten()) < 1.0
+
+
+def test_knn():
+    model = KNNRegressor(k=5)
+    model.fit(X_train, y_train)
+    predictions = model.predict(X_test)
+    assert mean_squared_error(y_test, predictions) < 10000